What is the net result of reducing the duration of a task (crashing) not on the critical path? A. Decreased project overhead costs B. Reduced likelihood of liquidated damages for late delivery C. Increased slack time associated with the task D. Reduction in the project duration 12) The idea of the value density calculation is: A. finding a carrier that can handle the weight B. matching the weight of the product with an appropriate carrier C. deciding where items should
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BankUSA Help Desk - Case Study Brent Schmitz Business 4208 Notre Dame de Namur July 28‚ 2013 Abstract The purpose of this case study is to recommend how to increase the overall effectiveness and improve the planning of the Help Desk business unit for BankUSA. This study will look at what are the service management characteristics of the customer service representative‚ create a suggested mission statement for the Help Desk and review which forecasting technique is best used by the
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estimates of what will happen in the future; this is the main purpose of forecasting. Some firms use subjective methods‚ seat-of-the pants methods‚ intuition‚ and experience. There are also several quantitative techniques‚ moving averages‚ exponential smoothing‚ trend projections‚ and least squares regression analysis. Eight steps to forecasting: * Determine the use of the forecast—what objective are we trying to obtain? * Select the items or quantities that are to be forecasted
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GM03 Quantitative Techniques for Managers Assignment No.I Assignment Code: 2011GM03A1 Last Date of Submission: 31st March 2011 Maximum Marks:100 Attempt all the questions. All the questions are compulsory and carry equal marks. Section-A Ques.1 In a certain examination there were 100 candidates of whom 21 failed‚ 6 secured distinctions‚ 43 were placed in third division and 18 in the second division. It is known that at least 75% marks are required for distinction‚ at least 40%
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BUS 305 Practice Exam 3 1) Assume the following time series data representing the number of sales per day your company’s employees make. Year-Quarter | t | Yt | 2001-1 | 1 | 17 | 2001-2 | 2 | 26 | 2001-3 | 3 | 21 | 2001-4 | 4 | 15 | 2002-1 | 5 | 19 | 2002-2 | 6 | 18 | 2002-3 | 7 | 21 | 2002-4 | 8 | 23 | a) Use Applet #16 to calculate the seasonal index numbers for the four quarters. b) Interpret what each of the four indices you computed in (a)
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the weighted 3-month moving average using weights of 0.50‚ 0.30‚ and 0.20 for periods 4-6. (5 points) c. Calculate the exponential smoothing forecast for periods 2-6 using an initial forecast (F1) 62‚ and an of 0.30. (10 points) d. Calculate the double exponential smoothing forecast for periods 2-6 using an initial trend forecast (T1) of 2.0‚ and initial exponential smoothing forecast (S1) of 60 an of
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..................................................................................... 8 4.2 WEIGHTED AVERAGE: ......................................................................................................................... 9 4.3 EXP. SMOOTHING: ............................................................................................................................... 9 4.4 COMPARISON BETWEEN MA AND ES ................................................................................
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PERENCANAAN & PENGENDALIAN PRODUKSI TIN 4113 Pertemuan 2 • Outline: – – – – – Karakteristik Peramalan Cakupan Peramalan Klasifikasi Peramalan Metode Forecast: Time Series Simple Time Series Models: • Moving Average (Simple & Weighted) • Referensi: – Smith‚ Spencer B.‚ Computer Based Production and Inventory Control‚ Prentice-Hall‚ 1989. – Tersine‚ Richard J.‚ Principles of Inventory and Materials Management‚ Prentice-Hall‚ 1994. – Pujawan‚ Demand Forecasting Lecture Note‚ IE-ITS‚ 2011
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FORECASTING FORECASTING The Role of the Manager Planning Organizing Staffing Leading Controlling Future ? Data Information • Short-range • Medium-range • Long-range Features Common to All Forecasts Forecasting techniques generally assume that same underlying causal system that existed in the past will continue to exist in the future. Forecasts are rarely perfect. Forecasts for groups of items tend to be more accurate than forecasts for individual items. Forecast
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203.50 3. EXPONENTIAL SMOOTHING: α=0.1 Ft+1 = α Xt + (1 - α ) Ft Let the starting point forecast be Jan sales‚ F1=200. And X1=200 Forecast for Feb‚ F2=200. Actaul Sales‚ X2=135 F3 (forecast for March)= α *X2+(1- α)*F2=0.1*135+0.9*200 In EXCEL: For the cell‚ F3 enter =$E$16*C2+(1-$E$16)*F2 where $E$16 is value of 0.1‚ which is the smoothing constant‚ α. So‚ the forecast for Dec is 205.56 The following shows the plot for Forecast by 3 MA‚ 5MA and Exponential Smoothing‚ 0.1 In last
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